{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a0004ef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import glob\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5bf9c2",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fc30511",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
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       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>000012.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>301458.SZ</th>\n",
       "      <th>688758.SH</th>\n",
       "      <th>301601.SZ</th>\n",
       "      <th>688583.SH</th>\n",
       "      <th>301602.SZ</th>\n",
       "      <th>688545.SH</th>\n",
       "      <th>001356.SZ</th>\n",
       "      <th>001395.SZ</th>\n",
       "      <th>688411.SH</th>\n",
       "      <th>920108.BJ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-04</th>\n",
       "      <td>2.3085</td>\n",
       "      <td>0.6579</td>\n",
       "      <td>16.7296</td>\n",
       "      <td>1.1559</td>\n",
       "      <td>3.8369</td>\n",
       "      <td>1.0886</td>\n",
       "      <td>1.5260</td>\n",
       "      <td>2.4750</td>\n",
       "      <td>3.9550</td>\n",
       "      <td>1.4578</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2002-01-07</th>\n",
       "      <td>2.2951</td>\n",
       "      <td>0.6498</td>\n",
       "      <td>16.7526</td>\n",
       "      <td>1.1274</td>\n",
       "      <td>3.8238</td>\n",
       "      <td>1.0679</td>\n",
       "      <td>1.4942</td>\n",
       "      <td>2.5125</td>\n",
       "      <td>3.8936</td>\n",
       "      <td>1.4233</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-08</th>\n",
       "      <td>2.2913</td>\n",
       "      <td>0.6432</td>\n",
       "      <td>16.6913</td>\n",
       "      <td>1.1157</td>\n",
       "      <td>3.8194</td>\n",
       "      <td>1.0603</td>\n",
       "      <td>1.4879</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>3.7001</td>\n",
       "      <td>1.4048</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-09</th>\n",
       "      <td>2.2665</td>\n",
       "      <td>0.6366</td>\n",
       "      <td>16.4233</td>\n",
       "      <td>1.1030</td>\n",
       "      <td>3.6264</td>\n",
       "      <td>1.0085</td>\n",
       "      <td>1.4624</td>\n",
       "      <td>2.5225</td>\n",
       "      <td>3.5160</td>\n",
       "      <td>1.3678</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-10</th>\n",
       "      <td>2.2513</td>\n",
       "      <td>0.6402</td>\n",
       "      <td>16.5458</td>\n",
       "      <td>1.0988</td>\n",
       "      <td>3.5914</td>\n",
       "      <td>1.0443</td>\n",
       "      <td>1.4720</td>\n",
       "      <td>2.5425</td>\n",
       "      <td>3.5396</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5392 columns</p>\n",
       "</div>"
      ],
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       "            000001.SZ  000002.SZ  000004.SZ  000006.SZ  000007.SZ  000008.SZ  \\\n",
       "date                                                                           \n",
       "2002-01-04     2.3085     0.6579    16.7296     1.1559     3.8369     1.0886   \n",
       "2002-01-07     2.2951     0.6498    16.7526     1.1274     3.8238     1.0679   \n",
       "2002-01-08     2.2913     0.6432    16.6913     1.1157     3.8194     1.0603   \n",
       "2002-01-09     2.2665     0.6366    16.4233     1.1030     3.6264     1.0085   \n",
       "2002-01-10     2.2513     0.6402    16.5458     1.0988     3.5914     1.0443   \n",
       "\n",
       "            000009.SZ  000010.SZ  000011.SZ  000012.SZ  ...  301458.SZ  \\\n",
       "date                                                    ...              \n",
       "2002-01-04     1.5260     2.4750     3.9550     1.4578  ...        NaN   \n",
       "2002-01-07     1.4942     2.5125     3.8936     1.4233  ...        NaN   \n",
       "2002-01-08     1.4879     2.5050     3.7001     1.4048  ...        NaN   \n",
       "2002-01-09     1.4624     2.5225     3.5160     1.3678  ...        NaN   \n",
       "2002-01-10     1.4720     2.5425     3.5396     1.3752  ...        NaN   \n",
       "\n",
       "            688758.SH  301601.SZ  688583.SH  301602.SZ  688545.SH  001356.SZ  \\\n",
       "date                                                                           \n",
       "2002-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-09        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-10        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            001395.SZ  688411.SH  920108.BJ  \n",
       "date                                         \n",
       "2002-01-04        NaN        NaN        NaN  \n",
       "2002-01-07        NaN        NaN        NaN  \n",
       "2002-01-08        NaN        NaN        NaN  \n",
       "2002-01-09        NaN        NaN        NaN  \n",
       "2002-01-10        NaN        NaN        NaN  \n",
       "\n",
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     "output_type": "display_data"
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     "data": {
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       "    <tr>\n",
       "      <th>2002-01-04</th>\n",
       "      <td>2.3218</td>\n",
       "      <td>0.6606</td>\n",
       "      <td>16.7557</td>\n",
       "      <td>1.1635</td>\n",
       "      <td>3.8232</td>\n",
       "      <td>1.0953</td>\n",
       "      <td>1.5331</td>\n",
       "      <td>2.4874</td>\n",
       "      <td>3.9717</td>\n",
       "      <td>1.4677</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2002-01-07</th>\n",
       "      <td>2.2911</td>\n",
       "      <td>0.6470</td>\n",
       "      <td>16.5815</td>\n",
       "      <td>1.1319</td>\n",
       "      <td>3.8332</td>\n",
       "      <td>1.0638</td>\n",
       "      <td>1.4943</td>\n",
       "      <td>2.4513</td>\n",
       "      <td>3.8915</td>\n",
       "      <td>1.4209</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-08</th>\n",
       "      <td>2.2994</td>\n",
       "      <td>0.6472</td>\n",
       "      <td>16.5742</td>\n",
       "      <td>1.1282</td>\n",
       "      <td>3.8379</td>\n",
       "      <td>1.0710</td>\n",
       "      <td>1.4957</td>\n",
       "      <td>2.5176</td>\n",
       "      <td>3.7615</td>\n",
       "      <td>1.4173</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-09</th>\n",
       "      <td>2.2746</td>\n",
       "      <td>0.6391</td>\n",
       "      <td>16.4265</td>\n",
       "      <td>1.1029</td>\n",
       "      <td>3.6467</td>\n",
       "      <td>1.0249</td>\n",
       "      <td>1.4646</td>\n",
       "      <td>2.4851</td>\n",
       "      <td>3.5287</td>\n",
       "      <td>1.3711</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-10</th>\n",
       "      <td>2.2081</td>\n",
       "      <td>0.6222</td>\n",
       "      <td>16.3362</td>\n",
       "      <td>1.0917</td>\n",
       "      <td>3.4892</td>\n",
       "      <td>1.0182</td>\n",
       "      <td>1.4408</td>\n",
       "      <td>2.5013</td>\n",
       "      <td>3.4182</td>\n",
       "      <td>1.3563</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5392 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000006.SZ  000007.SZ  000008.SZ  \\\n",
       "date                                                                           \n",
       "2002-01-04     2.3218     0.6606    16.7557     1.1635     3.8232     1.0953   \n",
       "2002-01-07     2.2911     0.6470    16.5815     1.1319     3.8332     1.0638   \n",
       "2002-01-08     2.2994     0.6472    16.5742     1.1282     3.8379     1.0710   \n",
       "2002-01-09     2.2746     0.6391    16.4265     1.1029     3.6467     1.0249   \n",
       "2002-01-10     2.2081     0.6222    16.3362     1.0917     3.4892     1.0182   \n",
       "\n",
       "            000009.SZ  000010.SZ  000011.SZ  000012.SZ  ...  301458.SZ  \\\n",
       "date                                                    ...              \n",
       "2002-01-04     1.5331     2.4874     3.9717     1.4677  ...        NaN   \n",
       "2002-01-07     1.4943     2.4513     3.8915     1.4209  ...        NaN   \n",
       "2002-01-08     1.4957     2.5176     3.7615     1.4173  ...        NaN   \n",
       "2002-01-09     1.4646     2.4851     3.5287     1.3711  ...        NaN   \n",
       "2002-01-10     1.4408     2.5013     3.4182     1.3563  ...        NaN   \n",
       "\n",
       "            688758.SH  301601.SZ  688583.SH  301602.SZ  688545.SH  001356.SZ  \\\n",
       "date                                                                           \n",
       "2002-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-09        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2002-01-10        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            001395.SZ  688411.SH  920108.BJ  \n",
       "date                                         \n",
       "2002-01-04        NaN        NaN        NaN  \n",
       "2002-01-07        NaN        NaN        NaN  \n",
       "2002-01-08        NaN        NaN        NaN  \n",
       "2002-01-09        NaN        NaN        NaN  \n",
       "2002-01-10        NaN        NaN        NaN  \n",
       "\n",
       "[5 rows x 5392 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_data = pd.read_csv(r'..\\daily_stock\\data_close_processed.csv')\n",
    "vwap_data = pd.read_csv(r'..\\daily_stock\\data_vwap_processed.csv')\n",
    "close_data.rename(columns={\"Unnamed: 0\": \"date\"}, inplace=True)\n",
    "close_data = close_data.set_index('date')\n",
    "vwap_data.rename(columns={\"Unnamed: 0\": \"date\"}, inplace=True)\n",
    "vwap_data = vwap_data.set_index('date')\n",
    "\n",
    "display(close_data.head())\n",
    "display(vwap_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "72d97b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "close_data.name = 'close'\n",
    "vwap_data.name = 'vwap'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c067dd18",
   "metadata": {},
   "source": [
    "# 10日及20日收益构造"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83db07c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "close_10\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5604 entries, 2002-01-04 to 2025-02-13\n",
      "Columns: 5362 entries, 000001.SZ to 920111.BJ\n",
      "dtypes: float64(5362)\n",
      "memory usage: 229.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>000012.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>301592.SZ</th>\n",
       "      <th>301626.SZ</th>\n",
       "      <th>301613.SZ</th>\n",
       "      <th>688726.SH</th>\n",
       "      <th>920088.BJ</th>\n",
       "      <th>301628.SZ</th>\n",
       "      <th>920066.BJ</th>\n",
       "      <th>301633.SZ</th>\n",
       "      <th>603205.SH</th>\n",
       "      <th>920111.BJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5604.000000</td>\n",
       "      <td>5604.000000</td>\n",
       "      <td>4058.000000</td>\n",
       "      <td>5604.000000</td>\n",
       "      <td>3788.000000</td>\n",
       "      <td>4121.000000</td>\n",
       "      <td>5604.000000</td>\n",
       "      <td>3474.000000</td>\n",
       "      <td>5241.000000</td>\n",
       "      <td>5604.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.005785</td>\n",
       "      <td>0.007935</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>0.008169</td>\n",
       "      <td>0.003666</td>\n",
       "      <td>0.005438</td>\n",
       "      <td>0.008605</td>\n",
       "      <td>-0.001420</td>\n",
       "      <td>0.006277</td>\n",
       "      <td>0.006990</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008745</td>\n",
       "      <td>0.008899</td>\n",
       "      <td>0.043773</td>\n",
       "      <td>0.054308</td>\n",
       "      <td>0.029930</td>\n",
       "      <td>-0.052086</td>\n",
       "      <td>0.030398</td>\n",
       "      <td>-0.021067</td>\n",
       "      <td>-0.019933</td>\n",
       "      <td>0.039694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.075411</td>\n",
       "      <td>0.084091</td>\n",
       "      <td>0.110311</td>\n",
       "      <td>0.101379</td>\n",
       "      <td>0.104841</td>\n",
       "      <td>0.096828</td>\n",
       "      <td>0.104884</td>\n",
       "      <td>0.113159</td>\n",
       "      <td>0.105482</td>\n",
       "      <td>0.098307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.143479</td>\n",
       "      <td>0.052821</td>\n",
       "      <td>0.049051</td>\n",
       "      <td>0.039434</td>\n",
       "      <td>0.048654</td>\n",
       "      <td>0.037483</td>\n",
       "      <td>0.021622</td>\n",
       "      <td>0.030610</td>\n",
       "      <td>0.062611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.301457</td>\n",
       "      <td>-0.274429</td>\n",
       "      <td>-0.446300</td>\n",
       "      <td>-0.414184</td>\n",
       "      <td>-0.622093</td>\n",
       "      <td>-0.327929</td>\n",
       "      <td>-0.463119</td>\n",
       "      <td>-0.481577</td>\n",
       "      <td>-0.564669</td>\n",
       "      <td>-0.331278</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.076153</td>\n",
       "      <td>-0.167438</td>\n",
       "      <td>-0.001644</td>\n",
       "      <td>-0.009067</td>\n",
       "      <td>-0.020458</td>\n",
       "      <td>-0.093433</td>\n",
       "      <td>-0.032895</td>\n",
       "      <td>-0.049216</td>\n",
       "      <td>-0.066308</td>\n",
       "      <td>-0.022038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.039853</td>\n",
       "      <td>-0.060145</td>\n",
       "      <td>-0.046670</td>\n",
       "      <td>-0.041678</td>\n",
       "      <td>-0.042530</td>\n",
       "      <td>-0.049805</td>\n",
       "      <td>-0.058718</td>\n",
       "      <td>-0.049069</td>\n",
       "      <td>-0.045811</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030982</td>\n",
       "      <td>-0.111404</td>\n",
       "      <td>0.013887</td>\n",
       "      <td>0.029982</td>\n",
       "      <td>0.003522</td>\n",
       "      <td>-0.085249</td>\n",
       "      <td>0.008860</td>\n",
       "      <td>-0.032800</td>\n",
       "      <td>-0.035656</td>\n",
       "      <td>0.001835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.003781</td>\n",
       "      <td>-0.000760</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001768</td>\n",
       "      <td>-0.016040</td>\n",
       "      <td>0.015755</td>\n",
       "      <td>0.052044</td>\n",
       "      <td>0.025781</td>\n",
       "      <td>-0.069972</td>\n",
       "      <td>0.031416</td>\n",
       "      <td>-0.025469</td>\n",
       "      <td>-0.016979</td>\n",
       "      <td>0.028751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.040301</td>\n",
       "      <td>0.049260</td>\n",
       "      <td>0.052598</td>\n",
       "      <td>0.051799</td>\n",
       "      <td>0.041187</td>\n",
       "      <td>0.038309</td>\n",
       "      <td>0.053289</td>\n",
       "      <td>0.044428</td>\n",
       "      <td>0.049594</td>\n",
       "      <td>0.049648</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007947</td>\n",
       "      <td>0.114301</td>\n",
       "      <td>0.049431</td>\n",
       "      <td>0.065797</td>\n",
       "      <td>0.044889</td>\n",
       "      <td>-0.040955</td>\n",
       "      <td>0.055557</td>\n",
       "      <td>-0.005961</td>\n",
       "      <td>-0.002555</td>\n",
       "      <td>0.066610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.412806</td>\n",
       "      <td>0.642405</td>\n",
       "      <td>0.945718</td>\n",
       "      <td>0.712544</td>\n",
       "      <td>0.699219</td>\n",
       "      <td>0.926254</td>\n",
       "      <td>0.602005</td>\n",
       "      <td>1.358108</td>\n",
       "      <td>0.854953</td>\n",
       "      <td>0.813641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048025</td>\n",
       "      <td>0.229834</td>\n",
       "      <td>0.140322</td>\n",
       "      <td>0.155607</td>\n",
       "      <td>0.105487</td>\n",
       "      <td>0.041892</td>\n",
       "      <td>0.077355</td>\n",
       "      <td>0.008452</td>\n",
       "      <td>0.020299</td>\n",
       "      <td>0.123309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 5362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         000001.SZ    000002.SZ    000004.SZ    000006.SZ    000007.SZ  \\\n",
       "count  5604.000000  5604.000000  4058.000000  5604.000000  3788.000000   \n",
       "mean      0.005785     0.007935     0.001662     0.008169     0.003666   \n",
       "std       0.075411     0.084091     0.110311     0.101379     0.104841   \n",
       "min      -0.301457    -0.274429    -0.446300    -0.414184    -0.622093   \n",
       "25%      -0.036788    -0.039853    -0.060145    -0.046670    -0.041678   \n",
       "50%       0.000000     0.000000    -0.001357     0.000000     0.000000   \n",
       "75%       0.040301     0.049260     0.052598     0.051799     0.041187   \n",
       "max       0.412806     0.642405     0.945718     0.712544     0.699219   \n",
       "\n",
       "         000008.SZ    000009.SZ    000010.SZ    000011.SZ    000012.SZ  ...  \\\n",
       "count  4121.000000  5604.000000  3474.000000  5241.000000  5604.000000  ...   \n",
       "mean      0.005438     0.008605    -0.001420     0.006277     0.006990  ...   \n",
       "std       0.096828     0.104884     0.113159     0.105482     0.098307  ...   \n",
       "min      -0.327929    -0.463119    -0.481577    -0.564669    -0.331278  ...   \n",
       "25%      -0.042530    -0.049805    -0.058718    -0.049069    -0.045811  ...   \n",
       "50%      -0.001050     0.000000    -0.003781    -0.000760     0.000000  ...   \n",
       "75%       0.038309     0.053289     0.044428     0.049594     0.049648  ...   \n",
       "max       0.926254     0.602005     1.358108     0.854953     0.813641  ...   \n",
       "\n",
       "       301592.SZ  301626.SZ  301613.SZ  688726.SH  920088.BJ  301628.SZ  \\\n",
       "count  10.000000  10.000000   9.000000   8.000000   8.000000   8.000000   \n",
       "mean   -0.008745   0.008899   0.043773   0.054308   0.029930  -0.052086   \n",
       "std     0.040213   0.143479   0.052821   0.049051   0.039434   0.048654   \n",
       "min    -0.076153  -0.167438  -0.001644  -0.009067  -0.020458  -0.093433   \n",
       "25%    -0.030982  -0.111404   0.013887   0.029982   0.003522  -0.085249   \n",
       "50%    -0.001768  -0.016040   0.015755   0.052044   0.025781  -0.069972   \n",
       "75%     0.007947   0.114301   0.049431   0.065797   0.044889  -0.040955   \n",
       "max     0.048025   0.229834   0.140322   0.155607   0.105487   0.041892   \n",
       "\n",
       "       920066.BJ  301633.SZ  603205.SH  920111.BJ  \n",
       "count   8.000000   6.000000   6.000000   4.000000  \n",
       "mean    0.030398  -0.021067  -0.019933   0.039694  \n",
       "std     0.037483   0.021622   0.030610   0.062611  \n",
       "min    -0.032895  -0.049216  -0.066308  -0.022038  \n",
       "25%     0.008860  -0.032800  -0.035656   0.001835  \n",
       "50%     0.031416  -0.025469  -0.016979   0.028751  \n",
       "75%     0.055557  -0.005961  -0.002555   0.066610  \n",
       "max     0.077355   0.008452   0.020299   0.123309  \n",
       "\n",
       "[8 rows x 5362 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "close_20\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5594 entries, 2002-01-04 to 2025-01-22\n",
      "Columns: 5352 entries, 000001.SZ to 301556.SZ\n",
      "dtypes: float64(5352)\n",
      "memory usage: 228.5+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
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     },
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     "output_type": "display_data"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>000012.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>920016.BJ</th>\n",
       "      <th>603091.SH</th>\n",
       "      <th>920099.BJ</th>\n",
       "      <th>301551.SZ</th>\n",
       "      <th>688615.SH</th>\n",
       "      <th>301618.SZ</th>\n",
       "      <th>001279.SZ</th>\n",
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       "      <th>301522.SZ</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5594.000000</td>\n",
       "      <td>5594.000000</td>\n",
       "      <td>4028.000000</td>\n",
       "      <td>5594.000000</td>\n",
       "      <td>3748.000000</td>\n",
       "      <td>4101.000000</td>\n",
       "      <td>5594.000000</td>\n",
       "      <td>3444.000000</td>\n",
       "      <td>5221.000000</td>\n",
       "      <td>5594.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.011643</td>\n",
       "      <td>0.016165</td>\n",
       "      <td>0.003190</td>\n",
       "      <td>0.016898</td>\n",
       "      <td>0.007729</td>\n",
       "      <td>0.011070</td>\n",
       "      <td>0.018414</td>\n",
       "      <td>-0.002697</td>\n",
       "      <td>0.012581</td>\n",
       "      <td>0.014268</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049691</td>\n",
       "      <td>-0.025924</td>\n",
       "      <td>-0.001307</td>\n",
       "      <td>0.134117</td>\n",
       "      <td>0.320414</td>\n",
       "      <td>0.050405</td>\n",
       "      <td>0.010367</td>\n",
       "      <td>0.018975</td>\n",
       "      <td>0.041832</td>\n",
       "      <td>0.189869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.106157</td>\n",
       "      <td>0.123629</td>\n",
       "      <td>0.154336</td>\n",
       "      <td>0.149613</td>\n",
       "      <td>0.147972</td>\n",
       "      <td>0.144933</td>\n",
       "      <td>0.158341</td>\n",
       "      <td>0.155373</td>\n",
       "      <td>0.150056</td>\n",
       "      <td>0.141916</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134949</td>\n",
       "      <td>0.025401</td>\n",
       "      <td>0.032849</td>\n",
       "      <td>0.158939</td>\n",
       "      <td>0.129554</td>\n",
       "      <td>0.057843</td>\n",
       "      <td>0.014402</td>\n",
       "      <td>0.066035</td>\n",
       "      <td>0.015707</td>\n",
       "      <td>0.081586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.362102</td>\n",
       "      <td>-0.329400</td>\n",
       "      <td>-0.458049</td>\n",
       "      <td>-0.520851</td>\n",
       "      <td>-0.651163</td>\n",
       "      <td>-0.404330</td>\n",
       "      <td>-0.552300</td>\n",
       "      <td>-0.549604</td>\n",
       "      <td>-0.597049</td>\n",
       "      <td>-0.429362</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208540</td>\n",
       "      <td>-0.066065</td>\n",
       "      <td>-0.049512</td>\n",
       "      <td>-0.138879</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>-0.093273</td>\n",
       "      <td>-0.006730</td>\n",
       "      <td>-0.064953</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>0.081791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.052793</td>\n",
       "      <td>-0.059293</td>\n",
       "      <td>-0.085938</td>\n",
       "      <td>-0.064909</td>\n",
       "      <td>-0.064229</td>\n",
       "      <td>-0.063155</td>\n",
       "      <td>-0.070388</td>\n",
       "      <td>-0.084403</td>\n",
       "      <td>-0.074095</td>\n",
       "      <td>-0.068731</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.064516</td>\n",
       "      <td>-0.042002</td>\n",
       "      <td>-0.021349</td>\n",
       "      <td>0.093217</td>\n",
       "      <td>0.235558</td>\n",
       "      <td>0.018477</td>\n",
       "      <td>0.001756</td>\n",
       "      <td>-0.017839</td>\n",
       "      <td>0.033330</td>\n",
       "      <td>0.179207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.003255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.008197</td>\n",
       "      <td>-0.003706</td>\n",
       "      <td>-0.015697</td>\n",
       "      <td>-0.003595</td>\n",
       "      <td>-0.001754</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083249</td>\n",
       "      <td>-0.029691</td>\n",
       "      <td>-0.008050</td>\n",
       "      <td>0.152254</td>\n",
       "      <td>0.331078</td>\n",
       "      <td>0.053432</td>\n",
       "      <td>0.003758</td>\n",
       "      <td>0.005424</td>\n",
       "      <td>0.036259</td>\n",
       "      <td>0.185038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.062267</td>\n",
       "      <td>0.075790</td>\n",
       "      <td>0.076117</td>\n",
       "      <td>0.075195</td>\n",
       "      <td>0.058995</td>\n",
       "      <td>0.053433</td>\n",
       "      <td>0.083317</td>\n",
       "      <td>0.055723</td>\n",
       "      <td>0.077542</td>\n",
       "      <td>0.080199</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129895</td>\n",
       "      <td>-0.009910</td>\n",
       "      <td>0.013043</td>\n",
       "      <td>0.227285</td>\n",
       "      <td>0.371306</td>\n",
       "      <td>0.089576</td>\n",
       "      <td>0.017670</td>\n",
       "      <td>0.035062</td>\n",
       "      <td>0.045043</td>\n",
       "      <td>0.191770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.516367</td>\n",
       "      <td>0.744061</td>\n",
       "      <td>1.179649</td>\n",
       "      <td>1.259681</td>\n",
       "      <td>0.865079</td>\n",
       "      <td>1.416719</td>\n",
       "      <td>0.836223</td>\n",
       "      <td>1.375676</td>\n",
       "      <td>1.145400</td>\n",
       "      <td>0.893045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.336940</td>\n",
       "      <td>0.034749</td>\n",
       "      <td>0.096820</td>\n",
       "      <td>0.421042</td>\n",
       "      <td>0.593679</td>\n",
       "      <td>0.146150</td>\n",
       "      <td>0.034818</td>\n",
       "      <td>0.161979</td>\n",
       "      <td>0.070751</td>\n",
       "      <td>0.311539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 5352 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         000001.SZ    000002.SZ    000004.SZ    000006.SZ    000007.SZ  \\\n",
       "count  5594.000000  5594.000000  4028.000000  5594.000000  3748.000000   \n",
       "mean      0.011643     0.016165     0.003190     0.016898     0.007729   \n",
       "std       0.106157     0.123629     0.154336     0.149613     0.147972   \n",
       "min      -0.362102    -0.329400    -0.458049    -0.520851    -0.651163   \n",
       "25%      -0.052793    -0.059293    -0.085938    -0.064909    -0.064229   \n",
       "50%       0.001205     0.000000    -0.003255     0.000000     0.000000   \n",
       "75%       0.062267     0.075790     0.076117     0.075195     0.058995   \n",
       "max       0.516367     0.744061     1.179649     1.259681     0.865079   \n",
       "\n",
       "         000008.SZ    000009.SZ    000010.SZ    000011.SZ    000012.SZ  ...  \\\n",
       "count  4101.000000  5594.000000  3444.000000  5221.000000  5594.000000  ...   \n",
       "mean      0.011070     0.018414    -0.002697     0.012581     0.014268  ...   \n",
       "std       0.144933     0.158341     0.155373     0.150056     0.141916  ...   \n",
       "min      -0.404330    -0.552300    -0.549604    -0.597049    -0.429362  ...   \n",
       "25%      -0.063155    -0.070388    -0.084403    -0.074095    -0.068731  ...   \n",
       "50%      -0.008197    -0.003706    -0.015697    -0.003595    -0.001754  ...   \n",
       "75%       0.053433     0.083317     0.055723     0.077542     0.080199  ...   \n",
       "max       1.416719     0.836223     1.375676     1.145400     0.893045  ...   \n",
       "\n",
       "       920016.BJ  603091.SH  920099.BJ  301551.SZ  688615.SH  301618.SZ  \\\n",
       "count  29.000000  24.000000  21.000000  20.000000  20.000000  18.000000   \n",
       "mean    0.049691  -0.025924  -0.001307   0.134117   0.320414   0.050405   \n",
       "std     0.134949   0.025401   0.032849   0.158939   0.129554   0.057843   \n",
       "min    -0.208540  -0.066065  -0.049512  -0.138879   0.056818  -0.093273   \n",
       "25%    -0.064516  -0.042002  -0.021349   0.093217   0.235558   0.018477   \n",
       "50%     0.083249  -0.029691  -0.008050   0.152254   0.331078   0.053432   \n",
       "75%     0.129895  -0.009910   0.013043   0.227285   0.371306   0.089576   \n",
       "max     0.336940   0.034749   0.096820   0.421042   0.593679   0.146150   \n",
       "\n",
       "       001279.SZ  920019.BJ  301522.SZ  301556.SZ  \n",
       "count   9.000000   9.000000   6.000000   5.000000  \n",
       "mean    0.010367   0.018975   0.041832   0.189869  \n",
       "std     0.014402   0.066035   0.015707   0.081586  \n",
       "min    -0.006730  -0.064953   0.027397   0.081791  \n",
       "25%     0.001756  -0.017839   0.033330   0.179207  \n",
       "50%     0.003758   0.005424   0.036259   0.185038  \n",
       "75%     0.017670   0.035062   0.045043   0.191770  \n",
       "max     0.034818   0.161979   0.070751   0.311539  \n",
       "\n",
       "[8 rows x 5352 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vwap_10\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5604 entries, 2002-01-04 to 2025-02-13\n",
      "Columns: 5362 entries, 000001.SZ to 920111.BJ\n",
      "dtypes: float64(5362)\n",
      "memory usage: 229.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>000012.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>301592.SZ</th>\n",
       "      <th>301626.SZ</th>\n",
       "      <th>301613.SZ</th>\n",
       "      <th>688726.SH</th>\n",
       "      <th>920088.BJ</th>\n",
       "      <th>301628.SZ</th>\n",
       "      <th>920066.BJ</th>\n",
       "      <th>301633.SZ</th>\n",
       "      <th>603205.SH</th>\n",
       "      <th>920111.BJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5340.000000</td>\n",
       "      <td>5324.000000</td>\n",
       "      <td>3772.000000</td>\n",
       "      <td>5340.000000</td>\n",
       "      <td>3060.000000</td>\n",
       "      <td>3739.000000</td>\n",
       "      <td>5315.000000</td>\n",
       "      <td>3318.000000</td>\n",
       "      <td>5010.000000</td>\n",
       "      <td>5419.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.005330</td>\n",
       "      <td>0.007460</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>0.006820</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>0.009029</td>\n",
       "      <td>-0.003330</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.005085</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002390</td>\n",
       "      <td>0.022567</td>\n",
       "      <td>0.048478</td>\n",
       "      <td>0.058751</td>\n",
       "      <td>0.029292</td>\n",
       "      <td>-0.060016</td>\n",
       "      <td>0.035301</td>\n",
       "      <td>-0.019582</td>\n",
       "      <td>-0.015066</td>\n",
       "      <td>0.026777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.073983</td>\n",
       "      <td>0.082780</td>\n",
       "      <td>0.111455</td>\n",
       "      <td>0.099714</td>\n",
       "      <td>0.109342</td>\n",
       "      <td>0.089352</td>\n",
       "      <td>0.101921</td>\n",
       "      <td>0.103092</td>\n",
       "      <td>0.104637</td>\n",
       "      <td>0.092486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035881</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.046186</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.047726</td>\n",
       "      <td>0.039779</td>\n",
       "      <td>0.042873</td>\n",
       "      <td>0.014484</td>\n",
       "      <td>0.025429</td>\n",
       "      <td>0.071942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.293059</td>\n",
       "      <td>-0.259066</td>\n",
       "      <td>-0.450734</td>\n",
       "      <td>-0.435781</td>\n",
       "      <td>-0.612225</td>\n",
       "      <td>-0.336030</td>\n",
       "      <td>-0.454873</td>\n",
       "      <td>-0.482850</td>\n",
       "      <td>-0.563026</td>\n",
       "      <td>-0.306330</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.055208</td>\n",
       "      <td>-0.147350</td>\n",
       "      <td>0.007165</td>\n",
       "      <td>0.001259</td>\n",
       "      <td>-0.019688</td>\n",
       "      <td>-0.098263</td>\n",
       "      <td>-0.026520</td>\n",
       "      <td>-0.039455</td>\n",
       "      <td>-0.046950</td>\n",
       "      <td>-0.024147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.037749</td>\n",
       "      <td>-0.042176</td>\n",
       "      <td>-0.062993</td>\n",
       "      <td>-0.047915</td>\n",
       "      <td>-0.051216</td>\n",
       "      <td>-0.043163</td>\n",
       "      <td>-0.050643</td>\n",
       "      <td>-0.060123</td>\n",
       "      <td>-0.050287</td>\n",
       "      <td>-0.045996</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026824</td>\n",
       "      <td>-0.101904</td>\n",
       "      <td>0.026916</td>\n",
       "      <td>0.042982</td>\n",
       "      <td>0.005434</td>\n",
       "      <td>-0.080907</td>\n",
       "      <td>0.007499</td>\n",
       "      <td>-0.029353</td>\n",
       "      <td>-0.035737</td>\n",
       "      <td>-0.009955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.000918</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.005781</td>\n",
       "      <td>-0.001429</td>\n",
       "      <td>-0.002096</td>\n",
       "      <td>-0.004879</td>\n",
       "      <td>-0.002370</td>\n",
       "      <td>-0.006277</td>\n",
       "      <td>-0.003789</td>\n",
       "      <td>-0.000162</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011464</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>0.027602</td>\n",
       "      <td>0.056353</td>\n",
       "      <td>0.020668</td>\n",
       "      <td>-0.072270</td>\n",
       "      <td>0.026307</td>\n",
       "      <td>-0.018963</td>\n",
       "      <td>-0.008401</td>\n",
       "      <td>-0.001034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.039867</td>\n",
       "      <td>0.050046</td>\n",
       "      <td>0.052532</td>\n",
       "      <td>0.050658</td>\n",
       "      <td>0.052191</td>\n",
       "      <td>0.037971</td>\n",
       "      <td>0.055408</td>\n",
       "      <td>0.045635</td>\n",
       "      <td>0.047454</td>\n",
       "      <td>0.048632</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018906</td>\n",
       "      <td>0.130809</td>\n",
       "      <td>0.046960</td>\n",
       "      <td>0.071556</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>-0.054595</td>\n",
       "      <td>0.064478</td>\n",
       "      <td>-0.007307</td>\n",
       "      <td>-0.001947</td>\n",
       "      <td>0.035697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.391588</td>\n",
       "      <td>0.624058</td>\n",
       "      <td>0.933030</td>\n",
       "      <td>0.742053</td>\n",
       "      <td>0.699165</td>\n",
       "      <td>0.804051</td>\n",
       "      <td>0.612664</td>\n",
       "      <td>1.013329</td>\n",
       "      <td>0.876540</td>\n",
       "      <td>0.520319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058680</td>\n",
       "      <td>0.235770</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>0.136694</td>\n",
       "      <td>0.138343</td>\n",
       "      <td>0.012073</td>\n",
       "      <td>0.103933</td>\n",
       "      <td>-0.003872</td>\n",
       "      <td>0.018002</td>\n",
       "      <td>0.133321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 5362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         000001.SZ    000002.SZ    000004.SZ    000006.SZ    000007.SZ  \\\n",
       "count  5340.000000  5324.000000  3772.000000  5340.000000  3060.000000   \n",
       "mean      0.005330     0.007460     0.000916     0.006820     0.004519   \n",
       "std       0.073983     0.082780     0.111455     0.099714     0.109342   \n",
       "min      -0.293059    -0.259066    -0.450734    -0.435781    -0.612225   \n",
       "25%      -0.037749    -0.042176    -0.062993    -0.047915    -0.051216   \n",
       "50%      -0.000918     0.000811    -0.005781    -0.001429    -0.002096   \n",
       "75%       0.039867     0.050046     0.052532     0.050658     0.052191   \n",
       "max       0.391588     0.624058     0.933030     0.742053     0.699165   \n",
       "\n",
       "         000008.SZ    000009.SZ    000010.SZ    000011.SZ    000012.SZ  ...  \\\n",
       "count  3739.000000  5315.000000  3318.000000  5010.000000  5419.000000  ...   \n",
       "mean      0.003484     0.009029    -0.003330     0.004309     0.005085  ...   \n",
       "std       0.089352     0.101921     0.103092     0.104637     0.092486  ...   \n",
       "min      -0.336030    -0.454873    -0.482850    -0.563026    -0.306330  ...   \n",
       "25%      -0.043163    -0.050643    -0.060123    -0.050287    -0.045996  ...   \n",
       "50%      -0.004879    -0.002370    -0.006277    -0.003789    -0.000162  ...   \n",
       "75%       0.037971     0.055408     0.045635     0.047454     0.048632  ...   \n",
       "max       0.804051     0.612664     1.013329     0.876540     0.520319  ...   \n",
       "\n",
       "       301592.SZ  301626.SZ  301613.SZ  688726.SH  920088.BJ  301628.SZ  \\\n",
       "count  10.000000  10.000000   9.000000   8.000000   8.000000   8.000000   \n",
       "mean   -0.002390   0.022567   0.048478   0.058751   0.029292  -0.060016   \n",
       "std     0.035881   0.142857   0.046186   0.042050   0.047726   0.039779   \n",
       "min    -0.055208  -0.147350   0.007165   0.001259  -0.019688  -0.098263   \n",
       "25%    -0.026824  -0.101904   0.026916   0.042982   0.005434  -0.080907   \n",
       "50%    -0.011464   0.004749   0.027602   0.056353   0.020668  -0.072270   \n",
       "75%     0.018906   0.130809   0.046960   0.071556   0.028927  -0.054595   \n",
       "max     0.058680   0.235770   0.130005   0.136694   0.138343   0.012073   \n",
       "\n",
       "       920066.BJ  301633.SZ  603205.SH  920111.BJ  \n",
       "count   8.000000   6.000000   6.000000   4.000000  \n",
       "mean    0.035301  -0.019582  -0.015066   0.026777  \n",
       "std     0.042873   0.014484   0.025429   0.071942  \n",
       "min    -0.026520  -0.039455  -0.046950  -0.024147  \n",
       "25%     0.007499  -0.029353  -0.035737  -0.009955  \n",
       "50%     0.026307  -0.018963  -0.008401  -0.001034  \n",
       "75%     0.064478  -0.007307  -0.001947   0.035697  \n",
       "max     0.103933  -0.003872   0.018002   0.133321  \n",
       "\n",
       "[8 rows x 5362 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vwap_20\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5594 entries, 2002-01-04 to 2025-01-22\n",
      "Columns: 5352 entries, 000001.SZ to 301556.SZ\n",
      "dtypes: float64(5352)\n",
      "memory usage: 228.5+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>000012.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>920016.BJ</th>\n",
       "      <th>603091.SH</th>\n",
       "      <th>920099.BJ</th>\n",
       "      <th>301551.SZ</th>\n",
       "      <th>688615.SH</th>\n",
       "      <th>301618.SZ</th>\n",
       "      <th>001279.SZ</th>\n",
       "      <th>920019.BJ</th>\n",
       "      <th>301522.SZ</th>\n",
       "      <th>301556.SZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5316.000000</td>\n",
       "      <td>5298.000000</td>\n",
       "      <td>3717.000000</td>\n",
       "      <td>5315.000000</td>\n",
       "      <td>2964.000000</td>\n",
       "      <td>3687.000000</td>\n",
       "      <td>5265.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>5378.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.010952</td>\n",
       "      <td>0.014674</td>\n",
       "      <td>0.002217</td>\n",
       "      <td>0.014593</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.005928</td>\n",
       "      <td>0.019308</td>\n",
       "      <td>-0.007437</td>\n",
       "      <td>0.009892</td>\n",
       "      <td>0.009827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042870</td>\n",
       "      <td>-0.025529</td>\n",
       "      <td>-0.003553</td>\n",
       "      <td>0.127263</td>\n",
       "      <td>0.325738</td>\n",
       "      <td>0.047502</td>\n",
       "      <td>0.013628</td>\n",
       "      <td>0.013652</td>\n",
       "      <td>0.037860</td>\n",
       "      <td>0.217515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.105209</td>\n",
       "      <td>0.120326</td>\n",
       "      <td>0.156306</td>\n",
       "      <td>0.147139</td>\n",
       "      <td>0.154738</td>\n",
       "      <td>0.123323</td>\n",
       "      <td>0.153402</td>\n",
       "      <td>0.134603</td>\n",
       "      <td>0.149974</td>\n",
       "      <td>0.134682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128908</td>\n",
       "      <td>0.025266</td>\n",
       "      <td>0.032889</td>\n",
       "      <td>0.157472</td>\n",
       "      <td>0.122062</td>\n",
       "      <td>0.059019</td>\n",
       "      <td>0.018799</td>\n",
       "      <td>0.054189</td>\n",
       "      <td>0.011120</td>\n",
       "      <td>0.130207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.365609</td>\n",
       "      <td>-0.324560</td>\n",
       "      <td>-0.471847</td>\n",
       "      <td>-0.532173</td>\n",
       "      <td>-0.650346</td>\n",
       "      <td>-0.416018</td>\n",
       "      <td>-0.494178</td>\n",
       "      <td>-0.522483</td>\n",
       "      <td>-0.600715</td>\n",
       "      <td>-0.413232</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.222809</td>\n",
       "      <td>-0.064821</td>\n",
       "      <td>-0.039106</td>\n",
       "      <td>-0.139306</td>\n",
       "      <td>0.085221</td>\n",
       "      <td>-0.101043</td>\n",
       "      <td>-0.003631</td>\n",
       "      <td>-0.040922</td>\n",
       "      <td>0.025927</td>\n",
       "      <td>0.099502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.054217</td>\n",
       "      <td>-0.062303</td>\n",
       "      <td>-0.090393</td>\n",
       "      <td>-0.067162</td>\n",
       "      <td>-0.074168</td>\n",
       "      <td>-0.064696</td>\n",
       "      <td>-0.071242</td>\n",
       "      <td>-0.086832</td>\n",
       "      <td>-0.076768</td>\n",
       "      <td>-0.070747</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.063643</td>\n",
       "      <td>-0.039853</td>\n",
       "      <td>-0.030561</td>\n",
       "      <td>0.089023</td>\n",
       "      <td>0.262262</td>\n",
       "      <td>0.027125</td>\n",
       "      <td>0.003011</td>\n",
       "      <td>-0.034859</td>\n",
       "      <td>0.029853</td>\n",
       "      <td>0.169223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001280</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>-0.011601</td>\n",
       "      <td>-0.004630</td>\n",
       "      <td>-0.009254</td>\n",
       "      <td>-0.012493</td>\n",
       "      <td>-0.006160</td>\n",
       "      <td>-0.017819</td>\n",
       "      <td>-0.010358</td>\n",
       "      <td>-0.001998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074452</td>\n",
       "      <td>-0.033245</td>\n",
       "      <td>-0.010245</td>\n",
       "      <td>0.141386</td>\n",
       "      <td>0.314396</td>\n",
       "      <td>0.047798</td>\n",
       "      <td>0.005863</td>\n",
       "      <td>0.011178</td>\n",
       "      <td>0.034998</td>\n",
       "      <td>0.177719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.062914</td>\n",
       "      <td>0.074399</td>\n",
       "      <td>0.077507</td>\n",
       "      <td>0.073858</td>\n",
       "      <td>0.069441</td>\n",
       "      <td>0.055034</td>\n",
       "      <td>0.085722</td>\n",
       "      <td>0.055656</td>\n",
       "      <td>0.074950</td>\n",
       "      <td>0.074089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153475</td>\n",
       "      <td>-0.009090</td>\n",
       "      <td>0.013617</td>\n",
       "      <td>0.209865</td>\n",
       "      <td>0.391626</td>\n",
       "      <td>0.070027</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.024516</td>\n",
       "      <td>0.047138</td>\n",
       "      <td>0.200705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.574866</td>\n",
       "      <td>0.758139</td>\n",
       "      <td>1.298846</td>\n",
       "      <td>1.189409</td>\n",
       "      <td>0.825888</td>\n",
       "      <td>1.047694</td>\n",
       "      <td>0.814436</td>\n",
       "      <td>0.962568</td>\n",
       "      <td>1.103629</td>\n",
       "      <td>0.884465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257883</td>\n",
       "      <td>0.022317</td>\n",
       "      <td>0.095471</td>\n",
       "      <td>0.392338</td>\n",
       "      <td>0.600648</td>\n",
       "      <td>0.146243</td>\n",
       "      <td>0.050994</td>\n",
       "      <td>0.133967</td>\n",
       "      <td>0.051916</td>\n",
       "      <td>0.440427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 5352 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         000001.SZ    000002.SZ    000004.SZ    000006.SZ    000007.SZ  \\\n",
       "count  5316.000000  5298.000000  3717.000000  5315.000000  2964.000000   \n",
       "mean      0.010952     0.014674     0.002217     0.014593     0.008100   \n",
       "std       0.105209     0.120326     0.156306     0.147139     0.154738   \n",
       "min      -0.365609    -0.324560    -0.471847    -0.532173    -0.650346   \n",
       "25%      -0.054217    -0.062303    -0.090393    -0.067162    -0.074168   \n",
       "50%       0.001280     0.000667    -0.011601    -0.004630    -0.009254   \n",
       "75%       0.062914     0.074399     0.077507     0.073858     0.069441   \n",
       "max       0.574866     0.758139     1.298846     1.189409     0.825888   \n",
       "\n",
       "         000008.SZ    000009.SZ    000010.SZ    000011.SZ    000012.SZ  ...  \\\n",
       "count  3687.000000  5265.000000  3279.000000  4981.000000  5378.000000  ...   \n",
       "mean      0.005928     0.019308    -0.007437     0.009892     0.009827  ...   \n",
       "std       0.123323     0.153402     0.134603     0.149974     0.134682  ...   \n",
       "min      -0.416018    -0.494178    -0.522483    -0.600715    -0.413232  ...   \n",
       "25%      -0.064696    -0.071242    -0.086832    -0.076768    -0.070747  ...   \n",
       "50%      -0.012493    -0.006160    -0.017819    -0.010358    -0.001998  ...   \n",
       "75%       0.055034     0.085722     0.055656     0.074950     0.074089  ...   \n",
       "max       1.047694     0.814436     0.962568     1.103629     0.884465  ...   \n",
       "\n",
       "       920016.BJ  603091.SH  920099.BJ  301551.SZ  688615.SH  301618.SZ  \\\n",
       "count  29.000000  24.000000  21.000000  20.000000  20.000000  18.000000   \n",
       "mean    0.042870  -0.025529  -0.003553   0.127263   0.325738   0.047502   \n",
       "std     0.128908   0.025266   0.032889   0.157472   0.122062   0.059019   \n",
       "min    -0.222809  -0.064821  -0.039106  -0.139306   0.085221  -0.101043   \n",
       "25%    -0.063643  -0.039853  -0.030561   0.089023   0.262262   0.027125   \n",
       "50%     0.074452  -0.033245  -0.010245   0.141386   0.314396   0.047798   \n",
       "75%     0.153475  -0.009090   0.013617   0.209865   0.391626   0.070027   \n",
       "max     0.257883   0.022317   0.095471   0.392338   0.600648   0.146243   \n",
       "\n",
       "       001279.SZ  920019.BJ  301522.SZ  301556.SZ  \n",
       "count   9.000000   9.000000   6.000000   5.000000  \n",
       "mean    0.013628   0.013652   0.037860   0.217515  \n",
       "std     0.018799   0.054189   0.011120   0.130207  \n",
       "min    -0.003631  -0.040922   0.025927   0.099502  \n",
       "25%     0.003011  -0.034859   0.029853   0.169223  \n",
       "50%     0.005863   0.011178   0.034998   0.177719  \n",
       "75%     0.020542   0.024516   0.047138   0.200705  \n",
       "max     0.050994   0.133967   0.051916   0.440427  \n",
       "\n",
       "[8 rows x 5352 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def build_return_dict(df_list, n_days_list):\n",
    "    return_dict = {}\n",
    "    for df in df_list:\n",
    "        for n_days in n_days_list:\n",
    "            key = f\"{df.name}_{n_days}\"\n",
    "            print(key)\n",
    "            return_dict[key] = ((df.shift(-n_days-1)/df.shift(-1))-1)\n",
    "            return_dict[key] = return_dict[key].replace({-np.inf:np.nan,np.inf:np.nan})\n",
    "            return_dict[key] = return_dict[key].dropna(axis=1,how='all').dropna(axis=0,how='all')    \n",
    "            display(return_dict[key].info())\n",
    "            display(return_dict[key].describe())\n",
    "\n",
    "    return return_dict\n",
    "\n",
    "# 计算20d收益\n",
    "return_dict = build_return_dict([close_data, vwap_data],[10,20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "989a685f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>000006.SZ</th>\n",
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       "      <th>301626.SZ</th>\n",
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       "      <th>920066.BJ</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>-0.045929</td>\n",
       "      <td>-0.028951</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.034281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.120271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022321</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>-0.066376</td>\n",
       "      <td>-0.057918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033853</td>\n",
       "      <td>0.009654</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.001320</td>\n",
       "      <td>-0.046692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.013024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164348</td>\n",
       "      <td>0.071925</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.021227</td>\n",
       "      <td>-0.077287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089215</td>\n",
       "      <td>-0.001643</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>-0.018141</td>\n",
       "      <td>-0.064822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.041099</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017192</td>\n",
       "      <td>-0.003856</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.134188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.156102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.135375</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.124466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.153471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.181176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.100002</td>\n",
       "      <td>0.177933</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-08</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.104975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.210277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.091211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209867</td>\n",
       "      <td>0.145785</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-09</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.293294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.187644</td>\n",
       "      <td>0.175680</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.432980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.158020</td>\n",
       "      <td>0.111101</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 5362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.SZ  000002.SZ  000004.SZ  000006.SZ  000007.SZ  000008.SZ  \\\n",
       "date                                                                           \n",
       "2010-01-04  -0.045929  -0.028951        NaN   0.034281        NaN        NaN   \n",
       "2010-01-05  -0.066376  -0.057918        NaN  -0.012949        NaN        NaN   \n",
       "2010-01-06   0.001320  -0.046692        NaN   0.013024        NaN        NaN   \n",
       "2010-01-07   0.021227  -0.077287        NaN  -0.049094        NaN        NaN   \n",
       "2010-01-08  -0.018141  -0.064822        NaN  -0.041099        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2010-07-06   0.000000   0.099284        NaN   0.134188        NaN        NaN   \n",
       "2010-07-07   0.000000   0.124466        NaN   0.153471        NaN        NaN   \n",
       "2010-07-08   0.000000   0.104975        NaN   0.210277        NaN        NaN   \n",
       "2010-07-09   0.000000   0.048444        NaN   0.293294        NaN        NaN   \n",
       "2010-07-12   0.000000   0.083123        NaN   0.432980        NaN        NaN   \n",
       "\n",
       "            000009.SZ  000010.SZ  000011.SZ  000012.SZ  ...  301592.SZ  \\\n",
       "date                                                    ...              \n",
       "2010-01-04   0.120271        NaN   0.022321   0.059230  ...        NaN   \n",
       "2010-01-05   0.114686        NaN   0.033853   0.009654  ...        NaN   \n",
       "2010-01-06   0.095817        NaN   0.164348   0.071925  ...        NaN   \n",
       "2010-01-07   0.000886        NaN   0.089215  -0.001643  ...        NaN   \n",
       "2010-01-08  -0.038929        NaN  -0.017192  -0.003856  ...        NaN   \n",
       "...               ...        ...        ...        ...  ...        ...   \n",
       "2010-07-06   0.156102        NaN   0.000000   0.135375  ...        NaN   \n",
       "2010-07-07   0.181176        NaN   0.100002   0.177933  ...        NaN   \n",
       "2010-07-08   0.091211        NaN   0.209867   0.145785  ...        NaN   \n",
       "2010-07-09   0.073461        NaN   0.187644   0.175680  ...        NaN   \n",
       "2010-07-12   0.071429        NaN   0.158020   0.111101  ...        NaN   \n",
       "\n",
       "            301626.SZ  301613.SZ  688726.SH  920088.BJ  301628.SZ  920066.BJ  \\\n",
       "date                                                                           \n",
       "2010-01-04        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-05        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-01-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2010-07-06        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-07-07        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-07-08        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-07-09        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2010-07-12        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            301633.SZ  603205.SH  920111.BJ  \n",
       "date                                         \n",
       "2010-01-04        NaN        NaN        NaN  \n",
       "2010-01-05        NaN        NaN        NaN  \n",
       "2010-01-06        NaN        NaN        NaN  \n",
       "2010-01-07        NaN        NaN        NaN  \n",
       "2010-01-08        NaN        NaN        NaN  \n",
       "...               ...        ...        ...  \n",
       "2010-07-06        NaN        NaN        NaN  \n",
       "2010-07-07        NaN        NaN        NaN  \n",
       "2010-07-08        NaN        NaN        NaN  \n",
       "2010-07-09        NaN        NaN        NaN  \n",
       "2010-07-12        NaN        NaN        NaN  \n",
       "\n",
       "[126 rows x 5362 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_df = return_dict['close_10'].loc['2010-01-04':'2010-07-12',:]\n",
    "display(sample_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e9af64bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.927832422586521)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df.dropna(axis=1,how = 'all').notna().sum().sum()/sample_df.dropna(axis=1,how = 'all').size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7b89757",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 假设 return_dict['vwap_10'] 是您的DataFrame\n",
    "def plot_valid_ratio(df):\n",
    "\n",
    "    # 计算每个chunk的有效数据比例\n",
    "    chunk_size = 126\n",
    "    results = []\n",
    "\n",
    "    for i in range(0, len(df), chunk_size):\n",
    "        chunk = df.iloc[i:i+chunk_size]\n",
    "        \n",
    "        # 计算有效数据比例\n",
    "        chunk_cleaned = chunk.dropna(axis=1, how='all')\n",
    "        valid_ratio = chunk_cleaned.notna().sum().sum() / chunk_cleaned.size\n",
    "        \n",
    "        results.append(valid_ratio)\n",
    "\n",
    "    # 转换为numpy数组方便处理\n",
    "    results = np.array(results)\n",
    "\n",
    "    # 创建可视化图表\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    # 绘制柱状图\n",
    "    x_pos = np.arange(len(results)) + 1  # 从1开始计数\n",
    "    bars = plt.bar(x_pos, results, color='skyblue', edgecolor='black')\n",
    "\n",
    "    # 添加平均线\n",
    "    mean_val = results.mean()\n",
    "    plt.axhline(mean_val, color='red', linestyle='--', \n",
    "                label=f'平均值: {mean_val:.2%}')\n",
    "\n",
    "    # 标记最高和最低点\n",
    "    max_val, min_val = results.max(), results.min()\n",
    "    plt.axhline(max_val, color='green', linestyle=':', alpha=0.3)\n",
    "    plt.axhline(min_val, color='orange', linestyle=':', alpha=0.3)\n",
    "\n",
    "    # 添加数值标签\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                f'{height:.1%}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "    # 图表美化\n",
    "    plt.title(f'每 {chunk_size} 行数据块的有效数据比例', fontsize=14, pad=20)\n",
    "    plt.xlabel('数据块序号', fontsize=12)\n",
    "    plt.ylabel('有效数据比例', fontsize=12)\n",
    "    plt.xticks(x_pos)\n",
    "    plt.ylim(0, 1.1)\n",
    "    plt.grid(axis='y', alpha=0.3)\n",
    "    plt.legend(loc='upper right')\n",
    "\n",
    "    # 显示图表\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 输出统计信息\n",
    "    print(f\"\\n统计分析 ({len(results)} 个数据块):\")\n",
    "    print(f\"平均有效比例: {mean_val:.2%}\")\n",
    "    print(f\"最高有效比例: {max_val:.2%} (第 {results.argmax()+1} 个数据块)\")\n",
    "    print(f\"最低有效比例: {min_val:.2%} (第 {results.argmin()+1} 个数据块)\")\n",
    "    print(f\"中位数: {np.median(results):.2%}\")\n",
    "    print(f\"标准差: {results.std():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8edf3e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "统计分析 (29 个数据块):\n",
      "平均有效比例: 92.93%\n",
      "最高有效比例: 98.23% (第 28 个数据块)\n",
      "最低有效比例: 76.08% (第 11 个数据块)\n",
      "中位数: 94.72%\n",
      "标准差: 0.0488\n"
     ]
    }
   ],
   "source": [
    "plot_valid_ratio(return_dict['vwap_10'].loc['2010-01-04':'2024-12-31',:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0cf91fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "统计分析 (29 个数据块):\n",
      "平均有效比例: 92.62%\n",
      "最高有效比例: 98.14% (第 28 个数据块)\n",
      "最低有效比例: 74.70% (第 11 个数据块)\n",
      "中位数: 94.09%\n",
      "标准差: 0.0519\n"
     ]
    }
   ],
   "source": [
    "plot_valid_ratio(return_dict['vwap_20'].loc['2010-01-04':'2024-12-31',:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d3c62ebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据总行数: 5604\n",
      "两个数据集都不缺失的行数: 5594\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "--------------------- 统计分析（双数据集共同有效部分） ---------------------\n",
      "数据块大小: 126 行\n",
      "分析数据块数量: 45 个\n",
      "平均有效比例: 92.6607%\n",
      "最高有效比例: 99.2339% (第 44 个数据块)\n",
      "最低有效比例: 78.7778% (第 26 个数据块)\n",
      "中位数: 94.7287%\n",
      "标准差: 0.050604\n",
      "============================================================\n",
      "\n",
      "前5个数据块的有效比例:\n",
      "数据块 1: 95.0620%\n",
      "数据块 2: 95.9255%\n",
      "数据块 3: 95.2840%\n",
      "数据块 4: 96.1144%\n",
      "数据块 5: 94.5124%\n"
     ]
    }
   ],
   "source": [
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "# 获取两个DataFrame并确保索引对齐\n",
    "df_10 = return_dict['vwap_10'].copy()\n",
    "df_20 = return_dict['vwap_20'].copy()\n",
    "\n",
    "# 找出两个DataFrame都不缺失的行索引\n",
    "common_index = df_10.dropna(how='all').index.intersection(df_20.dropna(how='all').index)\n",
    "print(f\"原始数据总行数: {len(df_10)}\")\n",
    "print(f\"两个数据集都不缺失的行数: {len(common_index)}\")\n",
    "\n",
    "# 提取都不缺失的数据\n",
    "df_common_10 = df_10.loc[common_index]\n",
    "df_common_20 = df_20.loc[common_index]\n",
    "\n",
    "# 计算每个chunk的有效数据比例\n",
    "chunk_size = 126\n",
    "results = []\n",
    "\n",
    "for i in range(0, len(common_index), chunk_size):\n",
    "    chunk_10 = df_common_10.iloc[i:i+chunk_size]\n",
    "    chunk_20 = df_common_20.iloc[i:i+chunk_size]\n",
    "    \n",
    "    # 找出两个chunk都不缺失的列\n",
    "    common_cols = chunk_10.dropna(axis=1, how='all').columns.intersection(\n",
    "                 chunk_20.dropna(axis=1, how='all').columns)\n",
    "    \n",
    "    # 计算有效数据比例\n",
    "    valid_count = chunk_10[common_cols].notna().sum().sum() + \\\n",
    "                 chunk_20[common_cols].notna().sum().sum()\n",
    "    total_cells = 2 * chunk_10[common_cols].size  # 乘以2是因为有两个DataFrame\n",
    "    \n",
    "    results.append(valid_count / total_cells)\n",
    "\n",
    "# 转换为numpy数组\n",
    "results = np.array(results)\n",
    "\n",
    "# 创建可视化图表\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# 绘制柱状图\n",
    "x_pos = np.arange(len(results)) + 1\n",
    "bars = plt.bar(x_pos, results, color='#66b3ff', edgecolor='#1a6fc9', alpha=0.8)\n",
    "\n",
    "# 添加平均线\n",
    "mean_val = results.mean()\n",
    "plt.axhline(mean_val, color='red', linestyle='--', linewidth=2, \n",
    "            label=f'平均值: {mean_val:.2%}')\n",
    "\n",
    "# 添加统计线\n",
    "plt.axhline(results.max(), color='green', linestyle=':', alpha=0.5, label='最大值')\n",
    "plt.axhline(results.min(), color='orange', linestyle=':', alpha=0.5, label='最小值')\n",
    "\n",
    "# 添加数值标签\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width()/2., height+0.02,\n",
    "             f'{height:.1%}', ha='center', va='bottom', \n",
    "             fontsize=9, fontweight='bold')\n",
    "\n",
    "# 图表美化\n",
    "plt.title(f'每 {chunk_size} 行数据块的有效数据比例（vwap_10和vwap_20都不缺失）', \n",
    "          fontsize=15, pad=20, fontweight='bold')\n",
    "plt.xlabel('数据块序号', fontsize=12, labelpad=10)\n",
    "plt.ylabel('有效数据比例', fontsize=12, labelpad=10)\n",
    "plt.xticks(x_pos, rotation=45 if len(results)>20 else 0)\n",
    "plt.ylim(0, min(1.2, max(1.1, results.max()+0.15)))\n",
    "plt.grid(axis='y', alpha=0.2, linestyle='--')\n",
    "plt.legend(loc='upper right', framealpha=0.9)\n",
    "\n",
    "# 显示图表\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 输出详细统计信息\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(f\"{' 统计分析（双数据集共同有效部分） ':-^60}\")\n",
    "print(f\"数据块大小: {chunk_size} 行\")\n",
    "print(f\"分析数据块数量: {len(results)} 个\")\n",
    "print(f\"平均有效比例: {mean_val:.4%}\")\n",
    "print(f\"最高有效比例: {results.max():.4%} (第 {results.argmax()+1} 个数据块)\")\n",
    "print(f\"最低有效比例: {results.min():.4%} (第 {results.argmin()+1} 个数据块)\")\n",
    "print(f\"中位数: {np.median(results):.4%}\")\n",
    "print(f\"标准差: {results.std():.6f}\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 输出前5个数据块的具体值\n",
    "print(\"\\n前5个数据块的有效比例:\")\n",
    "for i, ratio in enumerate(results[:5], 1):\n",
    "    print(f\"数据块 {i}: {ratio:.4%}\")\n"
   ]
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